Publications by authors named "Shant Raj Jnawali"

Healthy natural forests maintain and/or enhances carbon stock while also providing potential habitat and an array of services to wildlife including large carnivores such as the tiger. This study is the first of its kind in assessing relationships between above-ground biomass carbon stock, tiger density and occupancy probability and its status in protected areas, corridors, and forest connectivity blocks. The dataset used to assess the relationship were: (1) Converged posterior tiger density estimates from camera trap data derived from Bayesian- Spatially Explicit Capture-Recapture model from Chitwan National Park; (2) Site wise probability of tiger occupancy estimated across the Terai Arc Landscape and (3) Habitat wise above-ground biomass carbon stock estimated across the Terai Arc Landscape.

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Anthropogenic land-use change continues to be predicated as a major driver of terrestrial biodiversity loss for the rest of this century. It has been determined that the effect of climate change on wildlife population will accelerate the rate and process of decline of global vertebrate populations. We investigated wildlife composition, occupancy, and activity pattern along the larger climate resilient forests that serve as microrefugia for a wide range of species under the escalating climate change.

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The Himalayan red panda (), a recently confirmed distinct species in the red panda genus, is distributed in Nepal, India, Bhutan, and south Tibet. Nepal represents the westernmost distribution of the Himalayan red panda. This study aims to determine important habitat features influencing the distribution of red panda and recommend possible habitat corridors.

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The Himalayan red panda is an endangered mammal endemic to Eastern Himalayan and South Western China. Data deficiency often hinders understanding of their spatial distribution and habitat use, which is critical for species conservation planning. We used sign surveys covering the entire potential red panda habitat over 22,453 km2 along the mid-hills and high mountains encompassing six conservation complexes in Nepal.

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Red panda , an endangered habitat specialist, inhabits a narrow distribution range in bamboo abundance forests along mountain slopes in the Himalaya and Hengduan Mountains. However, their habitat use may be different in places with different longitudinal environmental gradients, climatic regimes, and microclimate. This study aimed to determine the habitat variables affecting red panda distribution across different longitudinal gradients through a multivariate analysis.

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In Nepal, the red panda (Ailurus fulgens) has been sparsely studied, although its range covers a wide area. The present study was carried out in the previously untapped Chitwan-Annapurna Landscape (CHAL) situated in central Nepal with an aim to explore current distributional status and identify key habitat use. Extensive field surveys conducted in 10 red panda range districts were used to estimate species distribution by presence-absence occupancy modeling and to predict distribution by presence-only modeling.

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Red pandas are known to be highly susceptible to endoparasites, which can have a prominent impact on the population dynamics of this endangered species. There are very limited published reports on prevalence and risk of parasites in wild populations of red panda, especially localized reports. This study attempts to provide an in-depth insight of the status of endoparasites in red pandas, which is critical for strengthening conservation efforts.

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The source populations of tigers are mostly confined to protected areas, which are now becoming isolated. A landscape scale conservation strategy should strive to facilitate dispersal and survival of dispersing tigers by managing habitat corridors that enable tigers to traverse the matrix with minimal conflict. We present evidence for tiger dispersal along transboundary protected areas complexes in the Terai Arc Landscape, a priority tiger landscape in Nepal and India, by comparing camera trap data, and through population models applied to the long term camera trap data sets.

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